TY  - UNPD
A1  - Dutt, Satyaji
A1  - Radermacher, Jan Wedigo
T1  - Age, wealth, and the MPC in Europe : a supervised machine learning approach
N2  - We investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) as a starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributions and retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.
T3  - SAFE working paper - No. 383 
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70138
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-701387
UR  - https://ssrn.com/abstract=4360002
N1  - Financial support from the Leibniz Institute for Financial Research SAFE is gratefully acknowledged.
VL  - February 13, 2023
PB  - SAFE
CY  - Frankfurt am Main
ER  -